P2P mesh-pull live video streaming applications -such as CoolStreaming, PPLive, and PPStream -have become popular in the recent years. In this paper, we examine the stream pollution attack, for which the attacker mixes polluted chunks into the P2P distribution, degrading the quality of the rendered media at the receivers. Polluted chunks received by an unsuspecting peer not only effect that single peer, but since the peer also forwards chunks to other peers, and those peers in turn forward chunks to more peers, the polluted content can potentially spread through much of the P2P network. The contribution of this paper is twofold. First, by way of experimenting and measuring a popular P2P live video streaming system, we show that the pollution attack can be devastating. Second, we evaluate the applicability of four possible defenses to the pollution attack: blacklisting, traffic encryption, hash verification, and chunk signing. Among these, we conclude that the chunk signing solutions are most suitable.

No abstract

Recently, researchers around the world in medical institutions and pharmaceutical companies are demanding a wider access to healthcare data for secondary use in order to provide enhanced and personalized medical services. For this purpose, healthcare information exchange between health authorities can be leveraged as a fundamental concept to meet these demands and enable the discovery of new insights and cures. However, health data are highly sensitive and private information that requires strong authentication and authorization procedures to manage the access to them. In this regard, the cloud paradigm has been used in these e-healthcare solutions, but they remain inefficient due to their inability to adapt to the expanding volume of data generated from body sensors and their vulnerability against cyberattacks. Hence, collaborative and distributed data governance supported by edge computing and blockchain promises enormous potentials in improving the performance and security of the whole system. In this paper, we present a secure and efficient data management framework, named "EdgeMediChain", for sharing health data. The proposed architecture leverages both edge computing and blockchain to facilitate and provide the necessary requirements for a healthcare ecosystem in terms of scalability, security, as well as privacy. The Ethereum-based testbed evaluations show the effectiveness of EdgeMediChain in terms of execution time with a reduction of nearly 84.75% for 2000 concurrent transactions, higher throughput compared to a traditional blockchain, and scalable ledger storage with a linear growth rate. INDEX TERMS Blockchain, data sharing, edge computing, electronic medical records, healthcare, internet of things, privacy, security, smart contracts.

In this paper we study the routing and wavelength converter placement problems in optical networks with sparse wavelength conversion. We propose a new dynamic routing algorithm with two new path cost functions based on the concept of the Least-Load Routing (LLR) with sparse converter placement. Moreover, we discuss the application of Genetic Algorithms (GA) to determine the optimal location of wavelength converters so that the call blocking probability is minimized. Simulation results show that the proposed dynamic routing algorithms performs significantly better than Shortest-Path (SP) routing and Fixed-Alternative-Routing (FAR), in terms of the call blocking probability. The GA model is able to obtain a nearly optimal solution of the wavelength converter placement problem within a reasonable time and its performance is better than that of two other popular heuristic placement algorithms. abstract